exact-inference

Exact Variable Elimination inference engine for discrete Bayesian Networks computing conditional posteriors and marginal probability distributions.

Exact Variable Elimination inference engine for discrete Bayesian Networks computing conditional posteriors and marginal probability distributions.

Bayesian Inference

Implementation of exact inference on Bayesian Network using Junction Trees in Python

Fast exact finite-sample back-testing for Value-at-Risk (VaR) models in R.

Exact bit-for-bit out-of-core inference for large dense LLMs — run a 52GB BF16 27B model on an 8GB GPU

Implementation of Prior, Rejection, Likelihood and Gibbs Sampling

This project implements both exact and approximate inference techniques for Bayesian Networks using enumeration and rejection sampling, respectively. It processes Bayesian Network structures in XMLBIF format, accepting command-line inputs to compute the posterior distribution of a query variable given observed evidence.

A from-scratch exact Bayesian-network inference engine (Enumeration-Ask, no probabilistic-reasoning library) in C#, wrapped in a Blazor WebAssembly + ASP.NET Core app for building networks and querying them in the browser

FAIKR MOD3 project

VaR backtests (Kupiec, Christoffersen, Basel traffic light) with exact finite-sample p-values